BIOSTAT M280 tentative schedule and handouts (expect frequent updates)
Readings:
| Week | Tuesday | Thursday | Homework |
|---|---|---|---|
| 1 | 4/2 introduction and course logistics [slides: ipynb, html] | 4/4 computer languages, Julia intro. [slides: ipynb, html] | |
| 2 | 4/9 plotting in Julia [slides: ipynb, html], Jupyter Notebook [slides: ipynb, html] | 4/11 computer arithmetic [slides: ipynb, html], algo. intro. [slides: ipynb, html] | HW1 [ipynb, html] |
| 3 | 4/16 BLAS [slides: ipynb, html], NLA on GPU [slides: ipynb, html], triangular systems [slides: ipynb, html] | 4/18 GE/LU [slides: ipynb, html] | HW2 [ipynb, html] |
| 4 | 4/23 Cholesky [slides: ipynb, html], QR (GS, Householder, Givens) [slides: ipynb, html] | 4/25 Sweep operator [slides: ipynb, html] | |
| 5 | 4/30 summary of linear regression [slides: ipynb, html], condition number [slides: ipynb, html], iterative methods intro [slides: ipynb, html] | 5/2 conjugate gradient [slides: ipynb, html], easy linear system [slides: ipynb, html] | HW3 [ipynb, html] |
| 6 | 5/7 eigen-decomposition and SVD [slides: ipynb, html] | 5/9 optimization intro. [slides: ipynb, html], optimization in Julia [slides: ipynb, html] | HW4 [ipynb, html] |
| 7 | 5/14 Newton-Raphson, Fisher scoring, GLM, nonlinear regression (Gauss-Newton) [slides: ipynb, html] | 5/16 quasi-Newton [slides: ipynb, html] | HW5 [ipynb, html] |
| 8 | 5/21 KKT [slides: ipynb, html], constrained optimization [slides: ipynb, html] | 5/23 EM algorithm [slides: ipynb, html] | HW6 [ipynb, html] |
| 9 | 5/28 MM algorithm [slides: ipynb, html] | 5/30 LP [slides: ipynb, html], QP [slides: ipynb, html] | |
| 10 | 6/4 SOCP [slides: ipynb, html], SDP [slides: ipynb, html], GP [slides: ipynb, html] | 6/6 concluding remarks [slides: ipynb, html] |